Open Recommender - An open source AI-powered recommendation system for videos and articles

🚀 Overview

Welcome to Open Recommender, an open source AI-powered recommendation system for videos and articles.

Understand your current interests by analyzing things like your Twitter engagement (likes, retweets, etc.) and other data sources.
Search the web for interesting content to recommend to you
Recommend interesting sections from videos
Recommend "timeless" content rather than "trending" content
Surface "niche bangers" - difficult to find but really high quality content
Biased towards learning as opposed to entertainment
Read my blog post for more: Building an LLM-Powered Open Source Recommendation System for YouTube
Create a system that borrows the best elements from YouTube Shorts, TikTok and incremental reading to create something that feels as effortless and engaging as a queue of YouTube shorts but actually helps you make progress towards meaningful goals

git clone this repo
cd open-recommender
yarn && yarn build
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
In the client, server and cli packages, cp .env.example .env and fill in the values
If you want to use Twitter as an input data source for recommendations, you need to create a fake Twitter account and create an accounts.txt file in the root folder with the account's credentials in the format username:password:email:email_password.

yarn server to run the backend
yarn client to run the frontend
yarn worker to run the background job worker
Open up the web client and click the login button in the top right.
After logging in using Twitter this will automatically trigger a new recommendations pipeline run task for the worker.
You can monitor a recommendations pipeline run using by navigating to http://localhost:5173/#/admin. Make sure you set ADMIN="Your Twitter Username" in the server .env file.
After a run is finished you can view your queue of recommendations by navigating to your feed.

A summary of the data pipeline: